Automatic 3D human body landmarks extraction and measurement based on mean curvature skeleton for tailoring
Bibliographic record
Abstract
Automatic 3D human body measurement is a crucial issue for tailoring and made-to-measure. This paper presents a novel framework for 3D human landmarks extraction and measurement. The proposed approach first segments the 3D human body into 13 parts by utilizing an improved Mean Curvature Skeleton (MCS) algorithm, in which we modify the Laplacian operator used in the original MCS with mesh saliency to enable the segmentation boundaries to be closer to the human joints. Based on the human segmentation, K-Nearest Neighbors, linear modeling, and geometric methods are employed to extract at least 21 landmarks. Many essential landmarks, such as acromion, elbow, crotch, etc., are extracted in new ways. To our best knowledge, this is the first paper to propose a generalized solution to approximate the elbow point for arbitrary arm poses in the automatic 3D human measurement systems. Subsequently, various anthropometric measurements can be calculated according to the landmarks extracted automatically. The proposed method is validated on the public datasets and our real scans, and the experimental results have verified that the proposed approach is efficient and effective in processing various 3D human bodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".